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PA1_template.Rmd

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----
-title: "Reproducible Research: Peer Assessment 1"
-output: 
-  html_document:
-    keep_md: true
----
+#Call the libraries
 
+library(tidyverse)
+library(mice)
 
-## Loading and preprocessing the data
+#Read the downloaded data from the desktop
 
+activity <- read.csv("C:/Users/zterjek/Desktop/activity.csv")
 
+#Calculate the total steps taken per day, visualize it with a histogram
 
-## What is mean total number of steps taken per day?
+perdaysum <- aggregate(steps ~ date, activity, sum)
 
+hist(perdaysum$steps, breaks = 20, col = "steelblue")
 
+#Calculate and report the average and median of total taken steps
 
-## What is the average daily activity pattern?
+perdaymean <- mean(perdaysum$steps)
 
+perdaymed <- median(perdaysum$steps)
 
+perdaymean
+perdaymed
 
-## Imputing missing values
+#Calculate the average steps taken by intervals, visualize it with a line chart
 
+perintervalmean <- aggregate(steps ~ interval, activity, mean)
 
+plot(perintervalmean$interval, perintervalmean$steps, type = "l", col = "darkred",
+     lwd = 1.5)
 
-## Are there differences in activity patterns between weekdays and weekends?
+#Calculate and report the interval when max steps were taken
+
+maxstepsperinterval <- max(perintervalmean$steps)
+
+maxstepsinterval <- filter(perintervalmean, steps == maxstepsperinterval)
+
+maxstepsinterval$interval
+
+#Calculate the missing values
+
+md.pattern(activity)
+
+#Impute the missing values with predictive mean matching
+
+activity_imputed <- data.frame(original = activity$steps,
+    imputed_steps = complete(mice(activity, method = "pmm"))$steps)
+
+activity_imputed$num <- 1:17568
+activity$num <- 1:17568
+
+merged_activity <- merge(activity, activity_imputed, by = "num")
+merged_activity <- merged_activity[,-c(1, 2)]
+merged_activity <- rename(merged_activity, original_steps = original)
+md.pattern(merged_activity)
+
+#Calculate the total steps taken per day of the imputed dataset, 
+#visualize it with a histogram
+
+perdaysum_merged <- aggregate(imputed_steps ~ date, merged_activity, sum)
+
+hist(perdaysum_merged$imputed_steps, breaks = 20, col = "darkgreen")
+
+#Calculate and report the average and median of total taken steps in the 
+#imputed dataset
+
+perdaymean_merged <- mean(perdaysum_merged$imputed_steps)
+
+perdaymed_merged <- median(perdaysum_merged$imputed_steps)
+
+perdaymean_merged
+perdaymed_merged
+
+#Checking the weekdays
+
+merged_activity$date <- as.Date(merged_activity$date)
+
+merged_activity$daytype <- as.factor(ifelse(weekdays(merged_activity$date) %in% 
+                        c("szombat", "vasárnap"), "Weekend", "Weekday"))
+
+#Calculate the average steps taken by intervals, visualize it with a line chart 
+#with weekdays and weekends separated
+
+grouped <- merged_activity %>% group_by(daytype, interval) %>%
+    summarize_at("imputed_steps", mean)
+
+ggplot(grouped, aes(interval, imputed_steps))+
+    geom_line(aes(col = daytype))+
+    facet_wrap(.~ grouped$daytype, nrow = 2, ncol = 1)

PA1_template.md

@@ -0,0 +1,180 @@
+RepData_PeerAssessment1_TZs
+Zsolt Terjek
+2023-06-03
+Calling the libraries
+
+library(tidyverse)
+## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
+## ✔ dplyr     1.1.1     ✔ readr     2.1.4
+## ✔ forcats   1.0.0     ✔ stringr   1.5.0
+## ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
+## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
+## ✔ purrr     1.0.1     
+## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
+## ✖ dplyr::filter() masks stats::filter()
+## ✖ dplyr::lag()    masks stats::lag()
+## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
+library(mice)
+## 
+## Attaching package: 'mice'
+## 
+## The following object is masked from 'package:stats':
+## 
+##     filter
+## 
+## The following objects are masked from 'package:base':
+## 
+##     cbind, rbind
+
+### Loading and preprocessing the data
+
+Reading the data downloaded to the desktop
+
+activity <- read.csv("C:/Users/zterjek/Desktop/activity.csv")
+
+### What is mean total number of steps taken per day?
+
+Calculate the total steps taken per day, visualize it with a histogram
+
+perdaysum <- aggregate(steps ~ date, activity, sum)
+
+hist(perdaysum$steps, breaks = 20, col = "steelblue")
+
+![image](https://github.com/zsoltterjek/RepData_PeerAssessment1/assets/128890269/0203fc3f-04d6-4292-9f11-cc0fd2fbecb5)
+
+Calculate and report the average and median of total taken steps
+
+perdaymean <- mean(perdaysum$steps)
+
+perdaymed <- median(perdaysum$steps)
+
+perdaymean
+## [1] 10766.19
+perdaymed
+## [1] 10765
+
+### What is the average daily activity pattern?
+
+Calculate the average steps taken by intervals, visualize it with a line chart
+
+perintervalmean <- aggregate(steps ~ interval, activity, mean)
+
+plot(perintervalmean$interval, perintervalmean$steps, type = "l", col = "darkred",
+     lwd = 1.5)
+
+![image](https://github.com/zsoltterjek/RepData_PeerAssessment1/assets/128890269/f43a4cd6-c32c-45ac-84e7-582726dd0c8c)
+
+Calculate and report the interval when max steps were taken
+
+maxstepsperinterval <- max(perintervalmean$steps)
+
+maxstepsinterval <- filter(perintervalmean, steps == maxstepsperinterval)
+
+maxstepsinterval$interval
+## [1] 835
+
+### Imputing missing values
+
+Calculate and report the interval when max steps were taken
+
+maxstepsperinterval <- max(perintervalmean$steps)
+
+maxstepsinterval <- filter(perintervalmean, steps == maxstepsperinterval)
+
+maxstepsinterval$interval
+## [1] 835
+Impute the missing values with predictive mean matching
+
+md.pattern(activity)
+
+![image](https://github.com/zsoltterjek/RepData_PeerAssessment1/assets/128890269/7e8abacf-6a3d-4ad6-b467-9148023bf00e)
+
+
+##       date interval steps     
+## 15264    1        1     1    0
+## 2304     1        1     0    1
+##          0        0  2304 2304
+
+activity_imputed <- data.frame(original = activity$steps,
+    imputed_steps = complete(mice(activity, method = "pmm"))$steps)
+## 
+##  iter imp variable
+##   1   1  steps
+##   1   2  steps
+##   1   3  steps
+##   1   4  steps
+##   1   5  steps
+##   2   1  steps
+##   2   2  steps
+##   2   3  steps
+##   2   4  steps
+##   2   5  steps
+##   3   1  steps
+##   3   2  steps
+##   3   3  steps
+##   3   4  steps
+##   3   5  steps
+##   4   1  steps
+##   4   2  steps
+##   4   3  steps
+##   4   4  steps
+##   4   5  steps
+##   5   1  steps
+##   5   2  steps
+##   5   3  steps
+##   5   4  steps
+##   5   5  steps
+## Warning: Number of logged events: 1
+
+activity_imputed$num <- 1:17568
+activity$num <- 1:17568
+
+merged_activity <- merge(activity, activity_imputed, by = "num")
+merged_activity <- merged_activity[,-c(1, 2)]
+merged_activity <- rename(merged_activity, original_steps = original)
+md.pattern(merged_activity)
+
+![image](https://github.com/zsoltterjek/RepData_PeerAssessment1/assets/128890269/0da5078f-626c-4930-b58d-c20397b2d572)
+
+##       date interval imputed_steps original_steps     
+## 15264    1        1             1              1    0
+## 2304     1        1             1              0    1
+##          0        0             0           2304 2304
+
+Calculate the total steps taken per day of the imputed dataset, visualize it with a histogram
+
+perdaysum_merged <- aggregate(imputed_steps ~ date, merged_activity, sum)
+
+hist(perdaysum_merged$imputed_steps, breaks = 20, col = "darkgreen")
+
+![image](https://github.com/zsoltterjek/RepData_PeerAssessment1/assets/128890269/cc144fcd-0ce5-4b9b-b6fb-b2d38f13ec86)
+
+Calculate and report the average and median of total taken steps in the imputed dataset
+
+perdaymean_merged <- mean(perdaysum_merged$imputed_steps)
+
+perdaymed_merged <- median(perdaysum_merged$imputed_steps)
+
+perdaymean_merged
+## [1] 10479.26
+perdaymed_merged
+## [1] 10439
+
+
+### Are there differences in activity patterns between weekdays and weekends?
+
+Checking the weekdays
+
+merged_activity$date <- as.Date(merged_activity$date)
+
+merged_activity$daytype <- as.factor(ifelse(weekdays(merged_activity$date) %in%                         c("szombat", "vasárnap"), "Weekend", "Weekday"))
+Calculate the average steps taken by intervals, visualize it with a line chart with weekdays and weekends separated
+
+grouped <- merged_activity %>% group_by(daytype, interval) %>%
+    summarize_at("imputed_steps", mean)
+
+ggplot(grouped, aes(interval, imputed_steps))+
+    geom_line(aes(col = daytype))+
+    facet_wrap(.~ grouped$daytype, nrow = 2, ncol = 1)
+
+![image](https://github.com/zsoltterjek/RepData_PeerAssessment1/assets/128890269/b5e182a5-7b8c-4aa7-aaac-0fb1819ed0a8)